Neural Mechanisms Supporting Implicit and Explicit Sensorimotor Learning

支持内隐和外显感觉运动学习的神经机制

基本信息

批准号：
10598457
负责人：
Jonathan Tsay
金额：
$ 1.89万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-01 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10598457
关键词：
Address Basal Ganglia Basic Science Behavior Behavior Control Behavioral Brain Calibration Cerebellum Clinical Closure by clamp Competence Complement Computer Models Conceptions Cues Delaware Dissociation Doctor of Philosophy Environment Evaluation Exhibits Faculty Fatigue Feedback Foundations Future Goals Grain Human Impairment Individual Injury Institution Intervention Learning Link Literature Memory Mentors Mentorship Methods Modeling Motor Movement Muscle Fatigue National Research Service Awards Neurologist Neuropsychology Parkinson Disease Participant Patients Pattern Physiological Play Positioning Attribute Postdoctoral Fellow Process Rehabilitation therapy Reporting Research Retrieval Rewards Rodent Model Role Savings Signal Transduction Solid Spinocerebellar Ataxias Strategic Planning Structure Supervision Support System System Testing Training Walking Work Writing analytical tool behavioral study brain machine interface cognitive neuroscience cognitive rehabilitation design experimental study flexibility improved insight motor control motor learning nervous system disorder neural neuroimaging neuromechanism nonhuman primate novel novel strategies operation pedagogy physical therapist power analysis professor recruit reward processing sensorimotor system skills success verbal

项目摘要

TITLE of PROJECT Neural Mechanisms Supporting Implicit and Explicit Sensorimotor Learning PROJECT SUMMARY Successful goal-directed actions require a flexible motor control system, one that can quickly respond to changes in the body (e.g., muscle fatigue) and in the environment (e.g., a windy day). Such flexibility depends on the operation of multiple learning processes. Implicit learning processes (i.e., implicit adaptation) keep the sensorimotor system exquisitely calibrated in an automatic manner, whereas explicit learning processes can facilitate rapid adjustments in a strategic, yet effortful manner. While the cerebellum and basal ganglia are prominently featured in the motor learning literature, their contribution to sensorimotor adaptation remains unclear, in part because past studies have employed tasks that conflate implicit and explicit learning processes. To disentangle the specific contributions of the cerebellum and basal ganglia to sensorimotor adaptation, I will use a set of behavioral tasks developed in my mentor’s lab that are designed to isolate the contribution of different learning processes. The results from this work have revised our current computational understanding of sensorimotor adaptation and have set the stage for taking a new look at the subcortical systems involved in this form of learning. In the proposed studies, we will test patients with spinocerebellar ataxia (SCA) and Parkinson’s disease (PD) on these tasks. In terms of basic research, the results will be important in advancing our understanding of how distributed neural systems support motor learning. In terms of translational benefit, the insights from this work will aid physical therapists to better tailor interventions that tap into intact learning mechanisms or enhance impaired ones. This NRSA F31 training plan encompasses two specific aims (three experiments) that will be conducted at UC Berkeley under the supervision of my sponsor, Prof. Richard Ivry. As a PI for 30 years, Prof. Ivry has trained 24 Ph.D. trainees and 21 post-doc fellows, many of whom hold faculty positions at research institutions. Under the supervision of Prof. Ivry, this proposal outlines a comprehensive training plan, centered on gaining fluency in computational modeling of behavior, methods in neuropsychology, writing and grantsmanship, presenting and disseminating research, and clinical pedagogy and mentorship. I will benefit from frequent interactions with Prof. Hyosub Kim, a former post-doc with Prof. Ivry who is now an Assistant Professor at the Univ. of Delaware. Prof. Kim provides added expertise in computational modeling and mentorship as a trained physical therapist. I will also benefit from mentorship provided by Prof. Robert Knight, a Professor and neurologist at UC Berkeley, who can provide additional training in patient evaluation and general training drawing from many years of stellar neuropsychological research with many patient groups and trainees. In summary, this training plan will build a solid foundation for my future role as a PI, working at the intersection of cognitive neuroscience and rehabilitation.

项目名称支持内隐和外显感觉运动学习的神经机制项目概要成功的目标导向行动需要灵活的电机控制系统，能够快速响应变化这种灵活性取决于身体（例如肌肉疲劳）和环境（例如大风天）。多个学习过程的运行（即隐式适应）感觉运动系统以自动方式精确校准，而显式学习过程可以小脑和基底神经节以战略性但努力的方式促进快速调整。它们在运动学习文献中占有突出地位，但它们对感觉运动适应的贡献仍然存在不清楚，部分原因是过去的研究采用了将内隐学习过程和外显学习过程混为一谈的任务。为了阐明小脑和基底神经节对感觉运动适应的具体贡献，我将使用我导师实验室开发的一组行为任务，旨在隔离不同人的贡献这项工作的结果修正了我们目前对学习过程的计算理解。感觉运动适应，并为重新审视参与这一过程的皮层下系统奠定了基础在拟议的研究中，我们将测试脊髓小脑共济失调 (SCA) 和帕金森病患者。就基础研究而言，这些结果对于推进我们的研究非常重要。了解分布式神经系统如何支持运动学习。这项工作的见解将帮助物理治疗师更好地制定干预措施，以促进完整的学习机制或增强受损机制。该 NRSA F31 培训计划包含将在 UC 进行的两个具体目标（三个实验）伯克利分校在我的资助者 Richard Ivry 教授的监督下作为 30 年来的 PI，Ivry 教授已经培训了 24 名学生。博士生和 21 名博士后研究员，其中许多人在研究机构担任教职。在 Ivry 教授的监督下，该提案概述了一个全面的培训计划，重点是熟练掌握行为的计算模型、神经心理学方法、写作和资助、呈现和与教授的频繁互动将使我受益于传播研究、临床教学法和指导。 Hyosub Kim 曾是 Ivry 教授的博士后，现在是特拉华大学的助理教授。作为一名训练有素的物理治疗师，Kim 提供了计算建模和指导方面的额外专业知识。还受益于加州大学伯克利分校教授和神经学家 Robert Knight 教授的指导，他可以提供患者评估方面的额外培训以及多年恒星经验的一般培训图纸与许多患者群体和受训者进行的神经心理学研究总之，该培训计划将建立一个为我未来作为 PI 的角色打下坚实的基础，在认知神经科学和康复的交叉领域工作。